A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with work scheduling, employee compensation, and employee turnover; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of employee turnover in relation to work scheduling and employee compensation; converts the predicted values of employee turnover in the database into percentages of observed values of employee turnover for a selected group of employers over a specified time period to create indices of employee turnover; and rank orders the selected employers according to their indices of employee turnover.